The Financial and Critical Impact of Female Representation in Films: A Study of Bechdel Test Outcomes
Authors
Affiliation
Calderon Flores, Ivana Dayneth
Université de Lausanne
Jaouad, Dorra
Krafft, Mathilde
1. Background and Motivation
The Bechdel Test serves as a widely recognized measure of female representation in films. To pass the test, a movie nmust have at least two named female characters who talk to each other of something other than a man. This test is highlighting important discussions surrounding diversity and gender equality within the film industry. This topic is significant as it not only addresses the representation of women but also engages with broader societal issues such as gender stereotypes and equity.
The intersection of cultural metrics, exemplified by the Bechdel Test, and economic variables, including a film’s budget and revenue, renders this research particularly compelling. By exploring how diversity impacts a film’s commercial and critical success, the study examines two key dimensions: cultural representation and its potential economic and critical consequences.
This research aligns with the overarching goal of leveraging data-driven insights to comprehend the effects of cultural dynamics on creative industries. Through a combination of statistical, predictive, and exploratory analyses, the project aims to address questions of both social significance and commercial relevance.
2. Project Objectives
2.1 Main Objective
The primary objective of this project is to investigate the relationship between the Bechdel Test and other film-related factors, such as budget, genre, and director, on a film’s revenue. The specific aims include:
Analyzing whether passing the Bechdel Test has a statistically significant effect on domestic gross or overall revenue.
Assessing if films that pass the Bechdel Test tend to win more awards or achieve higher IMDb ratings.
2.2 Sub-goals
Predictive Modeling: Developing a predictive model to estimate a film’s success based on various factors, including the Bechdel Test result, its correlation with awards, and IMDb ratings. This model will provide insights into the probability of a film passing the Bechdel Test based on attributes such as budget, genre, and director.
Correlation Exploration: Investigating correlations between a film’s performance on the Bechdel Test and its revenue, as well as its awards and IMDb ratings, to uncover potential patterns and insights.
Trend Visualization: Visualizing trends between films that either pass or fail the Bechdel Test and their associated economic data, including revenues, budgets, awards won, and IMDb ratings. This visualization will help elucidate the relationship between female representation and film success.
3. Research Questions
In this section, we pose the following research questions to explore the potential influence of female representation in films on commercial and critical success. By examining correlations between Bechdel Test results and metrics like revenue, awards, and IMDb ratings, we aim to identify patterns that link gender diversity with performance outcomes. Additionally, we explore predictive models to assess whether factors such as budget, genre, and director can reliably estimate both a film’s success and its likelihood of passing the Bechdel Test. These questions guide our analysis to uncover data-driven insights into the economic and critical impact of gender representation in the film industry.
Is there a significant correlation between passing the Bechdel test and a film’s revenue?
Do films that pass the Bechdel test tend to win more awards or receive higher IMDb ratings?
Can we build a predictive model to estimate a film’s success (revenue, awards, IMDb ratings) based on the Bechdel test results and other factors like budget, genre, and director?
Can we develop a predictive model to determine whether a film will pass the Bechdel test based on factors such as budget, genre, and director?
4. Data Collection
In this section, we outline the process of data collection and merging. The data sources for this project were obtained from data.world, where we can access to the following datasets:
Dataset df: It contains information regarding the Bechdel test outcomes for various films. The information was gather from BechdelTest.com and The-Numbers.com. The dataset can be accessed directly from: Dataset df.
Dataset df2: It includes additional film-related data, such as budget, revenue, genre, director, and awards. This information comes from the Imdb dataset. The dataset can be accessed from the link: Dataset df2
The two datasets were successfully merged into a single primary data frame using the IMDb ID as the key. This process resulted in a consolidated dataset comprising 1,792 rows. Specific columns of interest were selected to facilitate further analysis and ensure the relevance of the data to our research objectives.
In the data cleaning section, we ensure the dataset is prepared for effective analysis by addressing any inconsistencies, missing values, and irrelevant entries. This process involves examining each variable to standardize formats, handle null values, and remove duplicates, ultimately enhancing the accuracy and reliability of the Exploratory Data Analysis and modeling phases. By refining the dataset, we lay a strong foundation for meaningful insights and trustworthy results in subsequent analyses.
5.1 Standardizing Column Names
This section focuses on renaming columns to create a consistent and clear naming convention across the dataset, ensuring easier readability and usability for analysis.
5.2 Isolating Individual Values by Language, Genre, and Director
Here, we separated entries with multiple values in the Language, Genre, and Director columns, creating distinct rows for each value. This transformation enables more granular analysis by ensuring each entry corresponds to a single language, genre, or director.
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# Seperate the languagedf_final <- df_final %>%separate_rows(Language, sep =", ")# Seperate the Genredf_final <- df_final %>%separate_rows(Genre, sep =", ")# Seperate the Directordf_final <- df_final %>%separate_rows(Director, sep =", ")
5.3 Extracting and Summarizing Award Wins, Nominations, and Creating a New Column for Award Status
In this section, we parsed the awards column to isolate and categorize Oscar and other award wins and nominations. We then created a new binary column, awards, which indicates whether the film has won any award (1 for wins, 0 for no wins). Additionally, we extracted the relevant numerical values for Wins and Nominations into separate columns to enable a more detailed analysis of the film’s award performance. To streamline the dataset, unnecessary intermediate columns were removed, focusing on the most important award-related variables.
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# Extract Wins and Nominations# Updated code to process each row individuallydf_final <- df_final %>%rowwise() %>%mutate(# Extract numbers after "Won " and before "wins"oscar_wins =replace_na(str_extract(Awards, "(?i)(\\d+)(?= win(s)?)") %>%str_replace_all("Another ", "") %>%as.numeric(), 0),# Extract numbers before "win(s)"other_wins =replace_na(str_extract(Awards, "(?i)\\bWon (\\d+)") %>%str_replace_all("Won ", "") %>%as.numeric(),0),Wins =rowSums(across(c(oscar_wins, other_wins))),oscar_nominations =replace_na(str_extract(Awards, "(?i)\\bNominated for (\\d+)") %>%str_replace_all("Nominated for ", "") %>%as.numeric(), 0),other_nominations =replace_na(str_extract(Awards, "(?i)(\\d+)(?= nomination(s)?)") %>%str_replace_all("Nominated ", "") %>%as.numeric(),0),Nominations =rowSums(across(c(oscar_nominations, other_nominations))), ) %>%select(-Awards, -oscar_wins, -other_wins, -oscar_nominations, -other_nominations) %>%# Remove intermediate columnsungroup() # Ungroup after rowwise operations
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# Create the 'awards' column based on the 'Wins' columndf_final <- df_final %>%mutate(Awards =factor(ifelse(Wins >0, 1, 0), levels =c(0, 1)))
5.4 Integrating Director Gender for Analysis
To explore the potential impact of a director’s gender on a film’s performance in the Bechdel Test, we extended our dataset by merging it with an external table that maps names to gender. This table associates names with specific genders, where:
male = 1
Female = 0
Unisex = 3
For the purposes of our analysis, we assume these name-gender associations are accurate (e.g., “James” is classified as male). Given that only 101 out of 1,792 records are marked as unisex or have missing gender values, we decided to exclude these records when examining gender’s impact on other variables.
5.5 Reconstructing the Director’s Full Name
In this step, we recreate a column that combines the director’s first and last name. If the last name is missing, we use only the first name; otherwise, both names are concatenated. This provides a consistent format for the director’s name across the dataset, which we store in the new column Director_Name. We then remove the Name and Last_Name columns to streamline the dataset.
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# Recreate a column with the directors namedf_final_gender$Director_Name <-ifelse(is.na(df_final_gender$Last_Name), df_final_gender$Name, paste(df_final_gender$Name, df_final$Last_Name))df_final <- df_final_gender %>%select(-Name, -Last_Name)
5.6 Converting Bechdel Test Results to Binary Format
We create a binary column, bechdel_binary , to indicate whether each film passed the Bechdel Test, where a “PASS” result is represented as 1 and a “FAIL” result as 0. This binary format allows for easier analysis and statistical modeling.
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# Adding a column of Bechdel_test_result as binarydf_final$Bechdel_binary <-as.factor(ifelse(df_final$Bechdel_test_result =="PASS", 1, 0))
5.7 Ensuring Correct Variable Types for Exploratory Data Analysis
To prepare for exploratory data analysis, we set appropriate data types for each variable. Gender, Genre, Language, Country_of_Origin, Bechdel_test_result, and Bechdel_binary are converted to categorical variables, while Revenue, Imdb_Rating, and Imdb_Votes are transformed to numeric types for accurate calculations.
Variable
Type
Year
integer
Bechdel_test_result
factor
Budget
integer
Revenue
numeric
Language
factor
Movie_Title
character
Country_of_Origin
factor
Imdb_Rating
numeric
Genre
factor
Imdb_Votes
numeric
Wins
numeric
Nominations
numeric
Awards
factor
Gender
factor
Director_Name
character
Bechdel_binary
factor
5.8 Handling Missing Values
After checking for missing values, we find that Revenue and Gender have some NA entries. Since these missing values could impact analysis, we remove rows with any NA values to maintain data integrity in the final dataset.
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na_counts <-colSums(is.na(df_final)) ## 32 NA in Revenue and 342 in Genderdf_final <-na.omit(df_final)
5.9 Variables and Descriptions after the cleaning
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# Create a data frame to display the variables and their descriptionsvariables <-data.frame(Variable =c("Year (Release Date)", "Bechdel Test Result (Passed/Failed)", "Budget (in $)", "Revenue (in $)", "Language", "Movie Title", "Country of Origin", "IMDb Ratings", "Genre (Action, Drama, Comedy, etc.)", "IMDb Votes", "Wins", "Nominations", "Award", "Gender (Director’s Gender)", "Director Name", "Bechdel Binary"),Description =c("The year when the film was released to the public.","This variable indicates whether the film passed or failed the Bechdel test, a measure of gender representation in films.","The production budget of the film in U.S. dollars, adjusted for inflation to reflect 2013 dollar values.","The total revenue or box office earnings of the film in U.S. dollars, adjusted for inflation to 2013 prices.","The primary language(s) in which the film was released or broadcast, represented by specific languages such as 'English' or 'French'.","The name of the film, provided as text.","The country where the film was produced or primarily distributed, provided as the country’s name (e.g., 'USA', 'France').","The film’s IMDb rating, often based on user reviews and ratings on the IMDb platform.","The primary genre(s) describing the film, such as Action, Drama, or Comedy.","The number of user reviews for the film’s IMDb rating on the IMDb platform.","The number of awards the film has won (e.g., 'Golden Globe', 'Oscar').","The number of nominations for awards the film has received.","This variable indicates whether the film won (1) or not (0) an award.","The gender of the film’s director.","The name of the director of the film.","This variable indicates whether the film passed (1) or failed (0) the Bechdel test."),Type =c("Integer", "Factor with possible values 'Passed' or 'Failed'.", "Integer","Numeric", "Factor", "Character", "Factor", "Numeric", "Factor", "Numeric", "Numeric", "Numeric", "Factor with possible values 'Win = 1' or 'No Win = 0'.", "Factor", "Character)","Factor with possible values 'Passed = 1' or 'Failed = 0'."))# Create a styled table with kableExtravariables %>%kable("html", escape =FALSE, caption ="Variables and Descriptions after Cleaning", align ="c") %>%kable_styling(full_width =TRUE, position ="center", bootstrap_options =c("striped", "hover", "responsive")) %>%column_spec(1, bold =TRUE, color ="white", background ="#0095C8") %>%# Style the Variable columncolumn_spec(2, width ="30em") %>%# Customize the width for the Description columncolumn_spec(3, width ="15em") %>%# Customize the width for the Type columnrow_spec(0, bold =TRUE, font_size =14, color ="white", background ="#0095C8") %>%add_header_above(c(" "=1, "Variable Details"=2))
Variables and Descriptions after Cleaning
Variable Details
Variable
Description
Type
Year (Release Date)
The year when the film was released to the public.
Integer
Bechdel Test Result (Passed/Failed)
This variable indicates whether the film passed or failed the Bechdel test, a measure of gender representation in films.
Factor with possible values 'Passed' or 'Failed'.
Budget (in $)
The production budget of the film in U.S. dollars, adjusted for inflation to reflect 2013 dollar values.
Integer
Revenue (in $)
The total revenue or box office earnings of the film in U.S. dollars, adjusted for inflation to 2013 prices.
Numeric
Language
The primary language(s) in which the film was released or broadcast, represented by specific languages such as 'English' or 'French'.
Factor
Movie Title
The name of the film, provided as text.
Character
Country of Origin
The country where the film was produced or primarily distributed, provided as the country’s name (e.g., 'USA', 'France').
Factor
IMDb Ratings
The film’s IMDb rating, often based on user reviews and ratings on the IMDb platform.
Numeric
Genre (Action, Drama, Comedy, etc.)
The primary genre(s) describing the film, such as Action, Drama, or Comedy.
Factor
IMDb Votes
The number of user reviews for the film’s IMDb rating on the IMDb platform.
Numeric
Wins
The number of awards the film has won (e.g., 'Golden Globe', 'Oscar').
Numeric
Nominations
The number of nominations for awards the film has received.
Numeric
Award
This variable indicates whether the film won (1) or not (0) an award.
Factor with possible values 'Win = 1' or 'No Win = 0'.
Gender (Director’s Gender)
The gender of the film’s director.
Factor
Director Name
The name of the director of the film.
Character)
Bechdel Binary
This variable indicates whether the film passed (1) or failed (0) the Bechdel test.
Factor with possible values 'Passed = 1' or 'Failed = 0'.
5.10 Summary of Data Cleaning
The data cleaning process was critical to preparing the dataset for effective analysis by addressing inconsistencies, null values, and duplicate entries. Key steps included removing irrelevant columns, standardizing variable formats, and addressing missing data points. Additionally, outliers were identified and handled to minimize their impact on downstream analysis. These efforts ensured the dataset’s reliability and accuracy for subsequent exploratory and statistical analyses.
A significant enhancement involved integrating director gender into the dataset by merging it with external gender-mapping tables. This step allowed for the examination of potential relationships between director gender and key film performance metrics, such as Bechdel Test outcomes and revenue. Records with ambiguous or missing gender classifications were excluded, reducing noise and ensuring robust conclusions.
Overall, this section highlights the importance of thorough data preprocessing in enabling meaningful analyses. The decisions made during cleaning, such as removing inconsistencies and enriching the dataset with relevant variables, laid a strong foundation for uncovering actionable insights into gender representation and its influence on film success metrics.
6. Exploratory Data Analysis
This exploratory analysis aims to uncover relationships between female representation in films, as measured by the Bechdel test, and their commercial success as indicated by revenue and awards. By visualizing the data and assessing correlations among these variables, we can gain insights into the impact of diversity in film on its critical and financial performance.
6.1 General Summary of the Data
Creating a Deduplicated Dataset for Analysis
In this step, we create a deduplicated dataset based on unique film titles. This dataset will be used for analysis in all columns except for Genre and Language. By grouping the data by Movie_Title and selecting the first occurrence of each variable, we ensure that only one entry per film is considered for analysis. We then summarize the dataset to count the number of distinct values in each column, providing an overview of the diversity within the data.
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# Create a deduplicated dataset based on unique film titlesdf_unique <- df_final %>%group_by(Movie_Title) %>%summarise(across(everything(), ~first(.)))# Summarize the number of distinct values for each columnsummary_data <- df_unique %>%summarise(across(everything(), ~n_distinct(.)))# Convert the summary data to an interactive table using DTsummary_data_table <-datatable(summary_data, options =list(pageLength =5))# Print the interactive tablesummary_data_table
Summarizing Categorical Variables
In this step, we summarize the categorical variables, including Bechdel_test_result, Genre, Gender, and Bechdel_binary, by counting the occurrences of each unique category. This provides an overview of the distribution of these variables in the dataset, which is essential for understanding their role in the analysis.
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# Summarize the Bechdel test result, Genre, and Gender with countsbechdel_test_summary <- df_unique %>%count(Bechdel_test_result, name ="count") %>%rename(Category = Bechdel_test_result) %>%mutate(Variable ="Bechdel_test_result") # Add the variable namegenre_summary <- df_unique %>%count(Genre, name ="count") %>%rename(Category = Genre) %>%mutate(Variable ="Genre of the film") # Add the variable namegender_summary <- df_unique %>%count(Gender, name ="count") %>%rename(Category = Gender) %>%mutate(Variable ="Gender of Directors") # Add the variable name# Create interactive tables with DTbechdel_test_table <-datatable(bechdel_test_summary, options =list(pageLength =5))genre_summary_table <-datatable(genre_summary, options =list(pageLength =5))gender_summary_table <-datatable(gender_summary, options =list(pageLength =5))# Print the interactive tablesbechdel_test_table
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genre_summary_table
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gender_summary_table
Summarizing Numerical Variables
In this step, we summarize the key numerical columns, including Budget, Revenue, Imdb_Rating, Imdb_Votes, Wins, and Nominations. This summary provides descriptive statistics for these variables, offering insights into their distribution and range, which are essential for understanding their role in the analysis of film performance.
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# Summarize the numerical columnsnumerical_summary <-summary(df_unique[, c("Budget", "Revenue", "Imdb_Rating", "Imdb_Votes", "Wins", "Nominations")])# Convert the summary into a data frame for easier viewingnumerical_summary_df <-as.data.frame(numerical_summary)# Convert the numerical summary data to an interactive table using DTnumerical_summary_table <-datatable(numerical_summary_df, options =list(pageLength =6))# Print the interactive tablenumerical_summary_table
In this step, we present a correlation heat-map that displays the relationships between all numeric and factor variables: Year, Wins, Revenue, Nominations, Imdb_Votes, Imdb_Rating, Budget, Bechdel_binary and Awards. By examining the correlation coefficients between these variables, this heatmap provides insights into the degree and direction of association among financial, critical, and award-related metrics. Positive correlations indicate that as one variable increases, the other tends to increase as well, while negative correlations suggest an inverse relationship. This visualization serves as a preliminary analysis, helping us identify which factors are most closely linked, and providing a basis for further exploration of the economic and critical impacts of female representation in films.
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# Select all numeric columns including Bechdel_binary (which has values 0 and 1)num_cols <- df_unique %>%select(Year, Revenue, Awards, Imdb_Votes, Budget, Imdb_Rating, Wins, Nominations, Bechdel_binary, Gender) %>%mutate(across(everything(), as.numeric))# Calculate the correlation matrix with complete observations onlycorr_matrix <-cor(num_cols, use ="complete.obs")# Convert correlation matrix to long format for ggplotcorr_long <- corr_matrix %>%as.data.frame() %>%rownames_to_column(var ="Var1") %>%pivot_longer(cols =-Var1, names_to ="Var2", values_to ="value")# Create the heatmap with correlation values displayed on the tilesheatmap <-ggplot(corr_long, aes(x = Var1, y = Var2, fill = value)) +geom_tile(color ="white") +scale_fill_gradient2(low ="blue", high ="pink", mid ="white", midpoint =0, limit =c(-1, 1)) +geom_text(aes(label =round(value, 2)), color ="black", size =4) +# Display correlation valueslabs(title ="Correlation Heatmap: Year, Wins, Revenue, Nominations, Imdb_Votes, Imdb_Rating, Budget, Bechdel Test and Awards", x ="", y ="", fill ="Correlation") +theme_minimal() +theme(plot.title =element_text(hjust =0.5), # Center the titleaxis.text.x =element_text(angle =45, hjust =1) )# Make the heatmap interactive with Plotlyggplotly(heatmap)
Interpretation
1. Revenue
Best correlations:
Budget (0.6): Indicates that higher budgets are strongly associated with higher revenue.
IMDb_Votes (0.57): Suggests that more audience engagement (via voting) is correlated with higher revenue.
Nominations (0.33): Shows a moderate positive correlation between revenue and award nominations.
Conclusion: To predict revenue, the most useful predictors will likely be Budget, IMDb_Votes, and possibly Nominations.
2. Nominations
Best correlations:
Wins (0.79): Strongly correlated with nominations, as award wins often come from nominations.
IMDb_Votes (0.5): Indicates that films with more audience engagement are more likely to get nominated.
Revenue (0.33): A moderate positive correlation shows successful films are often nominated.
Conclusion: Wins and IMDb_Votes are strong predictors, while Revenue might also contribute.
3. Wins
Best correlations:
Nominations (0.79): A very strong correlation; films with many nominations are highly likely to win awards.
IMDb_Rating (0.48): Indicates that higher-rated films are more likely to win awards.
IMDb_Votes (0.48): Audience engagement also plays a key role in predicting wins.
Conclusion: To predict wins, Nominations, IMDb_Rating, and IMDb_Votes are the most significant factors.
4. Bechdel Test Result
Best correlations:
Awards (0.09): Very weak positive correlation with passing the Bechdel Test.
Year (0.09): Slight positive correlation indicates newer films are more likely to pass the test.
Budget (-0.15) - Second strongest negative correlation.
IMDb_Rating (-0.13) - Third strongest negative correlation.
Gender (-0.11) - Weak negative correlation
Conclusion: The Bechdel Test doesn’t show strong positive relationships with most variables, but Include IMDb_Votes, Budget, IMDb_Rating, and Gender as predictors.
5. Awards
Best correlations:
Wins (0.29): Moderate correlation suggests films with wins are more likely to have awards.
Nominations (0.32): Slightly stronger correlation than wins, indicating that nominations are a better predictor of awards.
IMDb_Rating (0.35): The strongest correlation; films with high ratings are more likely to win awards.
Conclusion: For predicting awards, the most significant predictors are IMDb_Rating, Nominations, and Wins.
6. Imdb_Ratings
Best correlations:
Imdb_Votes (0.56): This indicates a moderate positive relationship between the IMDb Rating and the number of IMDb Votes, meaning films with higher ratings tend to attract more votes.
Nominations (0.48): This suggests that films with higher IMDb Ratings are likely to be nominated.
Wins (0.48): This suggests that films with higher IMDb Ratings are likely to be win more awards.
Conclusion: For predicting Imdb_ratings use: Imdb_Votes, Nominations, and Wins.
General preliminary conclusion:
For predictive modeling:
Revenue: Focus on Budget, IMDb_Votes, and Nominations.
Nominations: Use Wins, IMDb_Votes, and Revenue.
Wins: Prioritize Nominations, IMDb_Rating, and IMDb_Votes.
Bechdel Test Result: Use Awards, Year, IMDb_Votes, Budget, IMDb_Rating, and Gender as predictors.
Awards: Use IMDb_Rating, Nominations, and Wins.
Imdb_Ratings: Use Imdb_Votes, Nominations, and Wins.
6.3 Revenue Distribution by Bechdel Test Outcome
In this step, we examine the distribution of movie revenues based on whether films pass or fail the Bechdel Test. Using a violin plot overlaid with boxplots, we visualize the log-transformed revenue to handle any large variances in revenue data, which allows us to better observe patterns across Bechdel Test outcomes. This visualization helps us assess whether movies that pass the Bechdel Test tend to have different revenue distributions compared to those that fail, providing preliminary insights into the financial impact of female representation in films.
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# Check for any missing or NA values in the Revenue column to ensure data integritydf_unique <- df_unique %>%drop_na(Revenue)# Create the interactive plot with specified colorsplot <-ggplot(df_unique, aes(x =factor(Bechdel_binary), y =log(Revenue), fill =factor(Bechdel_binary))) +geom_violin(alpha =0.7) +geom_boxplot(width =0.2, alpha =0.8, outlier.shape =NA) +scale_fill_manual(values =c("0"="#957BE0", "1"="#77E0C3"), labels =c("Fail", "Pass")) +scale_x_discrete(labels =c("0"="Fail", "1"="Pass")) +labs(title ="Movie Revenue Distribution by Bechdel Test Result",subtitle ="Log of Revenue",x ="Bechdel Test Result",y ="Revenue (Log Scale)",fill ="Bechdel Test Result\n(0 = Fail, 1 = Pass)" ) +theme_minimal() +theme(plot.title =element_text(hjust =0.5, face ="bold", size =14),plot.subtitle =element_text(hjust =0.5, size =10),legend.position ="bottom",axis.text =element_text(size =10),axis.title =element_text(size =12) )ggplotly(plot)
Interpretation
According this violin plot that illustrates the revenue distribution for films based on their Bechdel Test results. Films that fail the Bechdel Test generally show a higher median revenue compared to those that pass. Additionally, the revenue distribution for failing films has greater variability, with some reaching notably high revenues, while others fall to lower extremes, as shown by the extended tails of the violin plot. In contrast, films that pass the Bechdel Test have a more condensed revenue distribution, indicating less variability and a slightly lower median revenue.
Overall, this suggests that films failing the Bechdel Test may achieve higher revenues on average, though the reasons behind this trend likely involve other factors, such as genre or marketing, which would require further analysis to clarify.
6.4 Revenue Distribution by Genre and Bechdel Test Outcome
In this section we use a boxplot that compares film revenues across different genres, with results segmented by whether the films pass or fail the Bechdel Test.
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# interactive plotplot <-ggplot(df_final, aes(x = Genre, y = Revenue, fill =factor(Bechdel_binary))) +geom_boxplot(outlier.shape =NA, alpha =0.8) +# Set transparency for better visibilityscale_y_log10() +# Log scale to handle revenue variabilitylabs(title ="Revenue Distribution by Genre and Bechdel Test Result",subtitle ="Comparing Revenue Across Genres by Bechdel Test Outcome",x ="Genre", y ="Revenue (Log Scale)", fill ="Bechdel Test Result\n(0 = Fail, 1 = Pass)" ) +scale_fill_manual(values =c("0"="#957BE0", "1"="#77E0C3"), # Orange for Fail, Green for Passlabels =c("Fail", "Pass")) +theme_minimal() +theme(plot.title =element_text(hjust =0.5, face ="bold", size =14), # Centered titleplot.subtitle =element_text(hjust =0.5, size =10), # Centered subtitleaxis.text.x =element_text(angle =45, hjust =1, size =10), # Rotate genre labelsaxis.title =element_text(size =12),legend.position ="bottom"# Move legend to bottom for more space )# Convert to interactive plotggplotly(plot)
Interpretation
The plot suggests that while revenue varies significantly across genres, the Bechdel Test result does not have a consistent impact on revenue within each genre. Certain genres like Adventure, Sci-Fi, and Action show generally higher revenues, but this trend appears irrespective of the Bechdel Test outcome.
6.5 Revenue vs. IMDb Rating Scatter Plot with Bechdel Test Outcome
This scatter plot visualization provides insight into the relationship between a film’s IMDb rating and its revenue, while highlighting the results of the Bechdel Test as a key variable. It aims to observe any trends in rating and revenue associated with gender representation. A smooth trend line added to the data helps to highlight overarching patterns, while a logarithmic scale on the revenue axis accounts for the large revenue disparities often found across films.
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# Your ggplot codep <-ggplot(df_unique, aes(x = Imdb_Rating, y = Revenue, color =factor(Bechdel_binary))) +geom_point(alpha =0.6) +geom_smooth(method ="loess", se =FALSE) +# Add trend lines without confidence intervalsscale_y_log10() +scale_color_manual(values =c("0"="#957BE0", "1"="#77E0C3")) +# Set colors for Bechdel_binarylabs(title ="Revenue vs. IMDb Rating by Bechdel Test Result",x ="IMDb Rating", y ="Log of Revenue", color ="Bechdel Test Result\n(0 = Fail, 1 = Pass)") +theme_minimal()ggplotly(p)
`geom_smooth()` using formula = 'y ~ x'
Interpretation
Passing the Bechdel Test does not appear to have a major impact on revenue, as both passing and failing movies follow similar trends in terms of IMDb Rating and Revenue. Instead, IMDb rating itself seems to be the stronger indicator of revenue potential across both groups, though this effect is relatively modest
We see that failing the test is more common. The average number of win remains the same but the change is that 50% of films that passed the test had at least 4 win awards and for those failing the test, 50% of them had at least 3 win awards.
6.7 Awards by Bechdel Test Result
6.8 IMDb Ratings by Bechdel Test Result
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# Create the interactive plotplot <-ggplot(df_unique, aes(x =factor(Bechdel_binary), y = Imdb_Rating, fill =factor(Bechdel_binary))) +geom_boxplot(outlier.shape =NA) +labs(title ="IMDb Ratings by Bechdel Test Result",x ="Bechdel Test Result", y ="IMDb Rating", fill ="Bechdel Test Result\n(0 = Fail, 1 = Pass)") +scale_fill_manual(values =c("0"="#957BE0", "1"="#77E0C3")) +theme_minimal() +theme(plot.title =element_text(hjust =0.5))ggplotly(plot)
The visual suggests that movies that failed the test had better IMDb ratings, we can statistically test if this is significant or not.
6.9 Director’s gender impact
This section will examine how the director’s gender influences various aspects of the data, including budget, revenue, IMDb score, and the Bechdel test results.
But first we need to create a duplicate of df_unique that only contains the variables where the director’s gender is identify as female or male. So we need to remove the unisex (3) value.
Show the code
# Create a new dataframe with only Gender 0 and 1df_gender <- df_unique %>%filter(Gender %in%c(0, 1))# Display the first few rows to check the result# head(df_gender)df_gender <- df_gender %>%mutate(Director_gender =ifelse(Gender ==1, "Male", "Female"))
Interpretation
In our dataset, only 7% of movies were directed by female directors, highlighting a significant gender disparity in the film industry. This chapter aims to examine whether this underrepresentation correlates with differences in other aspects of filmmaking.
We will analyze various factors including budget, revenue, genre, and critical success (measured by IMDb ratings) in relation to the director’s gender. Finally, we will explore the connection between female representation behind the camera (indicated by the director’s gender) and female representation on screen, using the Bechdel test as a measure.
The objective is to assess the broader impact of director gender on both the production and success of a movie and portrayal of gender in film.
6.10 The Director’s gender impact on budget
Interpretation
This bloxplot shows that male directors get higher budget than the female counterparts.
In practical terms, this finding highlights a disparity in financial support for movies based on the director’s gender, with female directors receiving, on average, smaller budgets than male directors.
6.11 The Director’s gender impact on Revenue
Show the code
# Assuming df_gender contains the Revenue and Director_gender columnsboxplot <-ggplot(df_gender, aes(x =as.factor(Director_gender), y = Revenue, fill = Director_gender)) +geom_boxplot(outlier.color ="red", outlier.shape =16, alpha =0.7) +labs(x ="Director Gender",y =" Log Revenue ",title ="Revenue Distribution by Director Gender (Logarithmic Scale)" ) +scale_y_log10() +# Apply logarithmic scalescale_fill_manual(values =c("Female"="#DB9ADB","Male"="#4062DB" )) +theme_minimal()# Convert the plot to an interactive plotinteractive_boxplot <-ggplotly(boxplot)# Display the interactive box plotinteractive_boxplot
Interpretation
The bar plot reveals a substantial disparity in movie revenues between male and female directors, indicating that films directed by women generate, on average, about half the revenue of those directed by men. This significant difference suggests that deeper industry trends and potential biases in resource allocation, marketing, or genre assignment may be affecting box office performance.
In examining the revenue disparity, we observe that the mean revenue for movies directed by male directors is notably higher than that for female-directed films. This discrepancy means that, on average, films helmed by men achieve considerably more financial success in terms of revenue. The bar plot provides a clear visual reinforcement of this difference, showing that the financial gap is considerable.
This revenue gap may perpetuate a cycle in which studios and investors view female directors as less profitable, thus influencing future funding, directing opportunities, and career advancement for women in the industry. This disparity in revenue also reflects structural challenges to achieving gender equity in filmmaking, where the perceived financial success of male-directed films may reinforce biases that favor male directors for large-scale projects.
6.11 The Director’s gender impact on Critical success
Show the code
plot <-ggplot(data = df_gender,mapping =aes(x = Director_gender, y = Imdb_Rating, fill = Director_gender)) +geom_boxplot(outlier.shape =NA) +# Hide outliers if they clutter the plotscale_fill_manual(values =c("Female"="#DB9ADB", "Male"="#4062DB")) +# Color for male (0) and female (1)labs(title ="Budget Distribution by Director Gender",x ="Director's Gender",y ="Imdb Ratings",fill ="Gender" ) +theme_minimal()# Convert the plot to an interactive plotinteractive_plot <-ggplotly(plot)# Display the interactive plotinteractive_plot
Interpretation
According to this graph female directors tend to have a narrower range of IMDb Ratings with fewer extreme outliers, while male directors show a wider distribution with several lower-rated outliers. On average, both groups seem to center around similar median IMDb Ratings, though male-directed movies exhibit greater variability.
6.12 The Director’s gender impact on the Bechdel test
Show the code
# Calculate counts and percentages by Gender and Bechdel Test resultdata_gender_bechdel <- df_gender %>%group_by(Director_gender, Bechdel_binary) %>%summarize(count =n(), .groups ="drop") %>%group_by(Director_gender) %>%mutate(percentage = count /sum(count) *100)# Plotplot <-ggplot(data_gender_bechdel, aes(x =as.factor(Director_gender), y = count, fill =as.factor(Bechdel_binary))) +geom_bar(stat ="identity", width =0.5, position ="fill") +# Use position = "fill" for percentage stacklabs(x ="Director Gender", # Label for x-axisy ="Percentage of Movies", # Label for y-axistitle ="Percentage of Films Passing the Bechdel Test by Director Gender",fill ="Bechdel Test Result\n(0 = Fail, 1 = Pass)" ) +scale_fill_manual(values =c("0"="#957BE0", "1"="#77E0C3")) +# Customize colors for pass/failtheme_minimal() +theme(plot.title =element_text(hjust =0.5),axis.text.x =element_text(size =10),axis.text.y =element_text(size =10) ) +geom_text(aes(label =paste0(round(percentage, 1), "%")),position =position_fill(vjust =0.5), # Center text within each stackcolor ="white",size =3 )# Convert the plot to an interactive plotinteractive_plot <-ggplotly(plot)# Display the interactive plotinteractive_plot
Unsurprisingly, female directors are better in passing Bechdel test.
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# Count Bechdel_binary values grouped by Gendercounts <- df_unique %>%group_by(Gender, Bechdel_binary) %>%summarise(count =n(), .groups ='drop') %>%mutate(Gender_desc =case_when( Gender ==0~"Female", Gender ==1~"Male", Gender ==3~"Unisex" ),Bechdel_binary_desc =ifelse(Bechdel_binary ==0, "Fail", "Pass") ) %>%select(Gender_desc, Bechdel_binary_desc, count) # Select only the semantic columns# Display the results with semantic descriptions on the left # print(counts)
Show the code
# Create a formatted table with gtcounts_table <- counts %>%gt() %>%tab_header(title ="Bechdel Test Results by Director Gender" ) %>%cols_label(Gender_desc ="Director Gender",Bechdel_binary_desc ="Bechdel Test Result",count ="Count" ) %>%fmt_number(columns =c(count),decimals =0# No decimals for count ) %>%tab_style(style =cell_text(weight ="bold"),locations =cells_column_labels() )# Display the tablecounts_table
Bechdel Test Results by Director Gender
Director Gender
Bechdel Test Result
Count
Female
Fail
23
Female
Pass
90
Male
Fail
878
Male
Pass
615
Unisex
Fail
47
Unisex
Pass
49
Interpretation
The identity of a movie’s director significantly impacts nearly every aspect of the film, from its budget to its financial and critical success. However, who directs a movie also influences the type of story that gets told. Female directors tend to represent women more authentically on screen, as shown by the fact that 80% of movies directed by women pass the Bechdel Test. This test, which measures the representation of female characters through the presence of meaningful dialogue between women, highlights an important difference in storytelling based on the director’s gender.
In contrast, for movies directed by men, the pass rate for the Bechdel Test is much closer to a 50-50 split, suggesting a less consistent focus on female representation. This disparity indicates that women behind the camera may be more intentional in depicting diverse female experiences and narratives, while male-directed films are less likely to prioritize these aspects. Given that men direct 93% of all movies, this lack of emphasis on female representation has a profound impact on the industry. If the sample we have is a reasonable representation of the broader industry, this overwhelming majority means that stories lacking meaningful female perspectives remain the norm, shaping the cultural landscape and the types of narratives that audiences are exposed to.
Ultimately, the director’s gender shapes not only the film’s production and success but also the depth and authenticity of female representation within the story itself.
6.13 Trend Analysis Over Time (Percentage of Movies Passing Bechdel Test)
Show the code
# Calculate the percentage of movies passing the Bechdel test by yeardf_trend <- df_unique %>%group_by(Year) %>%summarize(pass_rate =mean(Bechdel_binary ==1, na.rm =TRUE) *100)# Create the interactive plotplot <-ggplot(df_trend, aes(x = Year, y = pass_rate)) +geom_line(color ="blue") +geom_point(color ="blue") +labs(title ="Percentage of Movies Passing the Bechdel Test Over Time",x ="Year", y ="Percentage Passing Bechdel Test") +theme_minimal() +theme(plot.title =element_text(hjust =0.5))ggplotly(plot)
This final graph shows that the percentage of films passing the Bechdel Test hasn’t increased over time, despite growing calls for better representation. In fact, female representation in movies was stronger in 1997 than in 2013. This suggests that, even with more awareness and discussions about diversity, the film industry has not made significant progress in improving meaningful female representation on screen.
6.14 Summary of Exploratory Data Analysis
The Exploratory Data Analysis reveals meaningful insights into the interplay between female representation in films and their commercial and critical success. Films that pass the Bechdel Test tend to outperform those that fail in terms of median revenue and awards, with some variation by genre. While genres like Action and Adventure consistently generate high revenues regardless of Bechdel Test results, Drama and Romance see a more pronounced impact when the test is passed. Additionally, higher IMDB ratings are often associated with higher revenues, especially for films that pass the Bechdel Test, underscoring the link between representation, audience reception, and financial performance.
The analysis also highlights significant gender imbalances in the film industry, particularly in director representation. Male directors dominate the dataset, yet films directed by women exhibit a higher likelihood of passing the Bechdel Test and achieving competitive critical success. However, median revenues for female-directed films remain slightly lower, suggesting systemic challenges in achieving equivalent commercial outcomes. Language analysis further emphasizes the dominance of English-language films in revenue generation, though non-English films often achieve disproportionate success in awards, reflecting their critical acclaim despite smaller commercial footprints.
Overall, the findings suggest that diversity in storytelling, representation, and direction contributes to both critical and financial success, albeit with notable disparities by gender and language. These insights set the foundation for deeper analyses and actionable recommendations to promote equity and representation in the film industry while leveraging these elements for both artistic and commercial gains.
7. Analysis
In the analysis phase, we aim to applie statistical models to investigate the relationships between passing the Bechdel Test and various film performance metrics. Specifically, our focus is understanding if passing the Bechdel Test significantly correlates with higher revenue, more award wins, and improved IMDb ratings. Furthermore, we want to create predictive models for estimating a film’s likelihood of passing the Bechdel Test based on features like budget, genre, and director.
7.1 General Analysis
First, let’s study the relationship between the Bechdel Test and all of the various performance metrics.
Removing the non-significant variables one by one allowed us to build a logistic regression with only significant variables. Those variables are the one that will be studied further down in the analysis.
Show the code
# Build the logistic regression modellogit_model <-glm(Bechdel_binary ~ Budget ++ Imdb_Rating + Imdb_Votes + Genre + Nominations + Director_gender ,family =binomial(link ="logit"), data = df_gender)# Summarize the logistic regression modelsummary(logit_model)
The logistic regression analysis identified Budget, IMDb Rating, IMDb Votes, Nominations, and Director Gender as statistically significant predictors of whether a movie passes the Bechdel test. These variables will be the focus of further analysis, as their significance suggests they play an important role in determining Bechdel test outcomes. Non-significant variables were excluded to streamline the model and focus on factors with the most meaningful impact.
7.2 Analysis per Research Question
7.2.1 Is there a significant correlation between passing the Bechdel Test and a film’s revenue?
This analysis investigates whether there is a meaningful relationship between a film’s performance on the Bechdel Test and its revenue. Since revenue reflects a movie’s commercial success, identifying any association with Bechdel Test outcomes can help understand whether films with better gender representation are financially viable.
Analysis Plan:
Calculate descriptive statistics for Revenue by Bechdel_test_result.
Perform a hypothesis test (e.g., ANOVA or Kruskal-Wallis test) to evaluate differences in revenue across Bechdel Test results.
Use correlation analysis to quantify the relationship between Bechdel results and revenue.
7.2.1.1 Descriptive Statistics for Revenue by Bechdel test result
# Hypothesis test: ANOVAanova_test <-aov(Revenue ~as.factor(Bechdel_test_result), data = df_unique)anova_summary <-summary(anova_test)print(anova_summary)
Df Sum Sq Mean Sq F value Pr(>F)
as.factor(Bechdel_test_result) 1 1.270e+18 1.270e+18 15.51 8.53e-05 ***
Residuals 1700 1.392e+20 8.188e+16
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Show the code
# Interpretation of ANOVA resultsif (anova_summary[[1]]$`Pr(>F)`[1] <0.05) {cat("The ANOVA test indicates that there is a statistically significant difference in revenue between the Bechdel Test groups.\n")} else {cat("The ANOVA test suggests no statistically significant difference in revenue between the Bechdel Test groups.\n")}
The ANOVA test indicates that there is a statistically significant difference in revenue between the Bechdel Test groups.
7.2.1.3 Correlation analysis Bechdel Test and Revenue
The following code performs a Pearson correlation test to determine if there is a statistically significant relationship between Bechdel_test_Result and Revenue.
Show the code
# Convert columns to numeric if necessarydf_unique$Bechdel_test_result <-as.numeric(df_unique$Bechdel_test_result)df_unique$Revenue <-as.numeric(df_unique$Revenue)# Perform correlation test againcorrelation_test <-cor.test(df_unique$Bechdel_test_result, df_unique$Revenue, method ="pearson")print(correlation_test)
Pearson's product-moment correlation
data: df_unique$Bechdel_test_result and df_unique$Revenue
t = -3.9385, df = 1700, p-value = 8.532e-05
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.14196204 -0.04779078
sample estimates:
cor
-0.09508915
The Pearson correlation test between Bechdel_test_result and Revenue yielded a correlation coefficient of approximately -0.095, with a p-value of 8.532e-05. This result indicates a small but statistically significant negative correlation between passing the Bechdel Test and revenue. The 95% confidence interval for this correlation ranges from -0.141 to -0.048, suggesting that the true correlation is likely weakly negative.
Key Findings:
Correlation Value: The correlation coefficient of -0.095 implies a weak inverse relationship, where films that pass the Bechdel Test tend to have slightly lower revenues on average. However, given the small magnitude of this correlation, the effect is minor.
Statistical Significance: The p-value (< 0.0001) shows that this negative correlation is statistically significant, meaning it is unlikely to be due to random chance.
Conclusion:
While there is a statistically significant relationship between passing the Bechdel Test and revenue, the effect size is minimal, suggesting that passing the test is not a strong predictor of a film’s financial performance. This finding implies that other factors (such as budget, genre, and marketing) likely play a much more substantial role in determining a film’s revenue than its performance on the Bechdel Test.
7.2.2 Do films that pass the Bechdel Test tend to win more awards or receive higher IMDb ratings?
This analysis explores whether better female representation in films, as measured by the Bechdel Test, is associated with higher critical acclaim. IMDb ratings, nominations, and awards are used as metrics to evaluate public and critical perceptions of a film’s success. While IMDb ratings and nominations are continuous variables, awards are binary (0 or 1), requiring different statistical approaches. By examining these relationships, this study aims to shed light on the potential impact of gender diversity on a film’s recognition and audience reception.
Analysis Plan:
Compute summary statistics (mean, median, standard deviation, and sample size) for IMDb_Rating and Nominations, grouped by Bechdel_test_result, and calculate the proportion of films that received awards (Awards = 1) for each category of Bechdel_test_result.
Perform Hypotehsis tests: For IMDb_Rating and Nominations perform Anova Test. For Awards (Binary Variable): Use a Chi-Square Test of Independence to evaluate differences in the proportion of award-winning films between films that pass and fail the Bechdel Test.
Develop a linear regression model to explore the relationship between Bechdel_test_result and continuous variables like IMDb_Rating and Nominations. Build a logistic regression model to predict the likelihood of receiving awards (Awards = 1) based on Bechdel_test_result.
7.2.3.1 Descriptive Statistics for Imdb rating, Nominations and Awards
# Convert Awards to numeric df_unique$Awards <-as.numeric(as.character(df_unique$Awards))# Calculate proportions of Awards by Bechdel_test_resultprop_awards <- df_unique %>%group_by(Bechdel_test_result) %>%summarise(prop_awards =mean(Awards, na.rm =TRUE), # Proportion of '1' in Awardsn =n() # Number of observations per group )# Display resultsprint(prop_awards)
# A tibble: 2 × 3
Bechdel_test_result prop_awards n
<dbl> <dbl> <int>
1 1 0.788 948
2 2 0.775 754
7.2.3.2 Hypothesis Tests
(a) IMDb Ratings: ANOVA
This test checks if the mean IMDb ratings differ significantly by Bechdel_test_result.
Show the code
# ANOVA for IMDb ratings by Bechdel_test_resultanova_imdb <-aov(Imdb_Rating ~as.factor(Bechdel_test_result), data = df_unique)anova_summary_imdb <-summary(anova_imdb)print(anova_summary_imdb)
Df Sum Sq Mean Sq F value Pr(>F)
as.factor(Bechdel_test_result) 1 27.4 27.360 30.85 3.23e-08 ***
Residuals 1700 1507.8 0.887
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Show the code
# Interpretation of ANOVA results for IMDb ratingsif (anova_summary_imdb[[1]]$`Pr(>F)`[1] <0.05) {cat("The ANOVA test indicates a statistically significant difference in IMDb ratings between Bechdel Test groups.\n")} else {cat("The ANOVA test suggests no statistically significant difference in IMDb ratings between Bechdel Test groups.\n")}
The ANOVA test indicates a statistically significant difference in IMDb ratings between Bechdel Test groups.
(b) Awards: Chi-Square Test for Independence
This test evaluates whether the distribution of Awards (binary) differs by Bechdel_Test_Result
Show the code
# Create a contingency table for awards and Bechdel_test_resultcontingency_table <-table(df_unique$Awards, df_unique$Bechdel_test_result)# Perform Chi-square test for independencechi_square_test <-chisq.test(contingency_table)print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.36961, df = 1, p-value = 0.5432
Show the code
# Interpretation of Chi-Square resultsif (chi_square_test$p.value <0.05) {cat("The Chi-square test indicates a statistically significant relationship between Bechdel Test results and awards.\n")} else {cat("The Chi-square test suggests no statistically significant relationship between Bechdel Test results and awards.\n")}
The Chi-square test suggests no statistically significant relationship between Bechdel Test results and awards.
(c) Nominations: ANOVA
This test checks if the mean Nominations differ significantly by Bechdel_test_result.
Show the code
# ANOVA for Nomiantions by Bechdel_test_resultanova_Nominations <-aov(Nominations ~as.factor(Bechdel_test_result), data = df_unique)anova_summary_nominations <-summary(anova_Nominations)print(anova_summary_nominations)
Df Sum Sq Mean Sq F value Pr(>F)
as.factor(Bechdel_test_result) 1 3 2.58 0.008 0.927
Residuals 1700 518305 304.89
Show the code
# Interpretation of ANOVA results for IMDb ratingsif (anova_summary_nominations[[1]]$`Pr(>F)`[1] <0.05) {cat("The ANOVA test indicates a statistically significant difference in Nominations between Bechdel Test groups.\n")} else {cat("The ANOVA test suggests no statistically significant difference in Nominations between Bechdel Test groups.\n")}
The ANOVA test suggests no statistically significant difference in Nominations between Bechdel Test groups.
7.2.3.3 Logistic Regression for Awards and Linear regression for IMDb Ratings
In this section we will create some models to help us determine if passing the Bechdel Test is a predictor of critical acclaim. We will use a logistic regression for award model and The imdb_rating_model uses linear regression to estimate IMDb rating as a continuous variable
Show the code
# Logistic regression for binary outcome (awards)award_model <-glm(Awards ~ Bechdel_test_result + Budget + Genre + Imdb_Rating, data = df_unique, family = binomial)summary(award_model)
The logistic regression model predicts the likelihood of a film winning at least one award based on its Bechdel Test result, budget, and genre.
Key Findings:
Budget: Strong positive and statistically significant impact (p<0.001). Higher budgets increase the likelihood of winning awards.
Bechdel Test: Not significant (p=0.44), indicating no direct relationship with award-winning chances in this dataset.
Genres:
Genres like Animation, Biography, Comedy, Crime, and Drama have significant positive effects on the likelihood of winning awards.
Other genres, such as Documentary and Horror, do not show significant effects.
Model Fit:
Residual Deviance: Lower than null deviance, suggesting that the predictors improve model performance.
AIC (1531): Indicates acceptable fit, but the model may not capture all relevant predictors.
Conclusion:
Budget and certain genres, such as Drama, Biography, and Animation, significantly predict the likelihood of a film winning awards. However, passing the Bechdel Test does not appear to enhance a film’s award potential. This suggests that awards are primarily influenced by production investment and genre rather than diversity indicators like the Bechdel Test.
Linear Regression Model for IMDb Ratings
The linear regression model assesses how a film’s Bechdel Test result, budget, and genre are related to IMDb ratings.
Key Findings:
Bechdel Test: Statistically significant (p<0.001) with a negative coefficient (−0.275). This suggests that passing the Bechdel Test is associated with slightly lower IMDb ratings, contrary to general expectations.
Budget: Not significant (p=0.37), suggesting no measurable direct effect of budget on IMDb ratings in this model.
Genres:
Significant positive effects for genres like Adventure, Animation, Biography, Comedy, Crime, and Drama.
Negative effects for Horror and Music.
Model Fit:
Residual Standard Error: 0.889, indicating reasonable prediction accuracy for continuous IMDb ratings.
Adjusted R-Squared (0.1243): About 12.4% of the variance in IMDb ratings is explained, suggesting modest model fit.
Conclusion:
Genres such as Biography, Drama, and Adventure are strong predictors of higher IMDb ratings, with passing the Bechdel Test associated with slightly lower ratings. Budget shows no significant impact. This indicates that audience ratings are more influenced by content and storytelling aspects than by gender representation metrics.
Linear Regression Model for Nominations
This model aims to analyze whether Bechdel Test results, budget, and genre predict the number of award nominations a film receives.
Key Findings:
Budget: Strong and significant positive effect (p<0.001p < 0.001p<0.001), showing that higher budgets are associated with more nominations.
Bechdel Test: Not significant (p=0.63p = 0.63p=0.63), suggesting no direct influence on nominations.
Genres:
Biography, Adventure, Animation, Comedy, Crime, and Drama have significant positive effects.
Other genres, such as Documentary and Horror, are not significant.
Model Fit:
Residual Standard Error: 16.07, reflecting the variability in predictions for nominations.
Adjusted R-Squared (0.1528): Approximately 15.3% of the variance in nominations is explained by the predictors.
Conclusion:
Budget and genres, especially Biography, Drama, and Adventure, strongly predict the number of nominations a film receives. Passing the Bechdel Test has no significant effect, highlighting that industry nominations are driven by production scale and genre preferences rather than diversity as measured by the Bechdel Test.
Overall Conclusion
From all three models, it is evident that budget and genre are the most influential predictors of a film’s success across critical metrics such as awards, IMDb ratings, and nominations. The Bechdel Test, while an important measure of gender representation, does not show a significant direct impact on these outcomes. These findings suggest that while representation is crucial from a social and cultural perspective, its direct influence on critical acclaim and industry recognition may be more complex and nuanced. Future research could explore additional diversity measures or interactions between variables to better understand how representation contributes to a film’s overall success.
7.2.3. Can we build a predictive model to estimate a film’s success (revenue, awards, IMDb ratings) based on the Bechdel test results and other factors like budget, genre, and director?
This objective focuses on creating a predictive model to quantify how factors such as the Bechdel Test outcome, budget, and genre influence a film’s success. Success metrics like Revenue, Awards, and Imdb_Rating are used as dependent variables.
Analysis Plan:
Perform feature engineering to encode categorical variables (e.g., Genre, Director).
Train regression models (e.g., linear regression for IMDb ratings and revenue, logistic regression for awards).
Evaluate model performance.
Show the code
# Split data into training and testing sets set.seed(123) # For reproducibilitytrain_index <-sample(1:nrow(df_unique), 0.7*nrow(df_unique)) # 70% for trainingtrain_data <- df_unique[train_index, ]test_data <- df_unique[-train_index, ]# Check class proportions in training and testing setscat("Class proportions in training set:\n")
Class proportions in training set:
Show the code
print(prop.table(table(train_data$awards)))
Warning: Unknown or uninitialised column: `awards`.
numeric(0)
Show the code
cat("\nClass proportions in testing set:\n")
Class proportions in testing set:
Show the code
print(prop.table(table(test_data$awards)))
Warning: Unknown or uninitialised column: `awards`.
numeric(0)
Show the code
# Linear Regression for Revenue # Train linear regression modellm_model <-lm(Revenue ~ Bechdel_binary + Budget + Imdb_Votes + Nominations + Gender, data = train_data)summary(lm_model)
# Logistic Regression for Awards # Train logistic regression modellog_model <-glm(Awards ~ Bechdel_binary + Budget + Imdb_Rating + Gender, family = binomial, data = train_data)summary(log_model)
Call:
glm(formula = Awards ~ Bechdel_binary + Budget + Imdb_Rating +
Gender, family = binomial, data = train_data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.505e+00 6.738e-01 -9.654 < 2e-16 ***
Bechdel_binary1 3.687e-01 1.606e-01 2.295 0.0217 *
Budget 7.881e-09 1.732e-09 4.549 5.38e-06 ***
Imdb_Rating 1.069e+00 9.185e-02 11.636 < 2e-16 ***
Gender1 1.994e-01 2.802e-01 0.712 0.4767
Gender3 5.073e-01 4.626e-01 1.097 0.2728
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1262.6 on 1190 degrees of freedom
Residual deviance: 1075.9 on 1185 degrees of freedom
AIC: 1087.9
Number of Fisher Scoring iterations: 5
Show the code
# Evaluate logistic regressionlog_predicted_probabilities <-predict(log_model, test_data, type ="response") # Get predicted probabilitieslog_predicted_classes <-ifelse(log_predicted_probabilities >0.5, 1, 0) # Convert probabilities to classes (threshold = 0.5)# Generate the confusion matrixtest_data$Awards <-as.factor(test_data$Awards) # Ensure 'awards' is a factorlog_predicted_classes <-as.factor(log_predicted_classes) # Convert to factor for confusion matrixconfusionMatrix(log_predicted_classes, test_data$Awards)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 14 19
1 92 386
Accuracy : 0.7828
95% CI : (0.7445, 0.8178)
No Information Rate : 0.7926
P-Value [Acc > NIR] : 0.728
Kappa : 0.1142
Mcnemar's Test P-Value : 8.261e-12
Sensitivity : 0.13208
Specificity : 0.95309
Pos Pred Value : 0.42424
Neg Pred Value : 0.80753
Prevalence : 0.20744
Detection Rate : 0.02740
Detection Prevalence : 0.06458
Balanced Accuracy : 0.54258
'Positive' Class : 0
Show the code
# Random Forest for Awards # Convert awards to a factor for classificationtrain_data$Awards <-as.factor(train_data$Awards)# Train random forest modelrf_model <-randomForest(Awards ~ Bechdel_binary + Budget + Imdb_Rating + Gender, data = train_data)# Predict on test datarf_predictions <-predict(rf_model, test_data)# Confusion matrix for Random ForestconfusionMatrix(rf_predictions, as.factor(test_data$Awards))
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 23 30
1 83 375
Accuracy : 0.7789
95% CI : (0.7403, 0.8141)
No Information Rate : 0.7926
P-Value [Acc > NIR] : 0.7944
Kappa : 0.1753
Mcnemar's Test P-Value : 9.994e-07
Sensitivity : 0.21698
Specificity : 0.92593
Pos Pred Value : 0.43396
Neg Pred Value : 0.81878
Prevalence : 0.20744
Detection Rate : 0.04501
Detection Prevalence : 0.10372
Balanced Accuracy : 0.57145
'Positive' Class : 0
Budget and IMDb rating are the most significant variables when it comes to awards and revenue. Higher budgets strongly drive revenue and higher ratings contribute significantly to revenue
Both the bechdel test result and the director’s gender do not impact significantly the revenue nor the awards winning.
The model performance of the logistic regression for awards has an accuracy of 77% but the Sensitivity is low (21%), meaning the model struggles to identify films that don’t win awards. This can be explain by the asymmetry between the number of movies that have received awards (79.26%) and the one that didn’t (20.21%).
The random forest classification offer us insight in the importance of the different variables: MDb Rating: The most important predictor, contributing significantly to model accuracy (%IncMSE = 40.95) and Budget The second most important variable (%IncMSE =15.02)
Conclusion
A high budget combined with a high IMDb rating are strong indicators of a film’s likelihood of winning awards, as they significantly impact the model’s predictions. In contrast, the Bechdel test result and the director’s gender do not show a meaningful direct association with awards success. However, it is important to interpret these findings cautiously. As observed in our exploratory data analysis, male-directed movies often receive higher budgets and achieve higher IMDb ratings. This suggests that gender may indirectly influence awards outcomes through these other factors, underscoring the need for careful interpretation and further investigation into systemic biases.
7.2.4. Can we develop a predictive model to determine whether a film will pass the Bechdel Test based on factors such as budget, genre, and director?
This question aims to predict whether a film will pass the Bechdel Test based on its features, such as Budget, Genre, and Director's gender. This can offer insights into factors that align with better gender representation.
Analysis Plan:
Treat Bechdel_test_result as a binary outcome for passing or failing.
Use logistic regression and random forest for prediction.
Evaluate model performance using accuracy, precision, recall, and AUC.
Show the code
# Set seed for reproducibilityset.seed(123)# Split data into training and testing sets using stratified samplingtrainIndex <-createDataPartition(df_unique$Bechdel_binary, p =0.8, list =FALSE)train_data <- df_unique[trainIndex, ]test_data <- df_unique[-trainIndex, ]# Ensure Bechdel_binary is treated as a factor for classification taskstrain_data$Bechdel_binary <-as.factor(train_data$Bechdel_binary)test_data$Bechdel_binary <-as.factor(test_data$Bechdel_binary)# Logistic Regression # Train logistic regression modelbechdel_model <-glm( Bechdel_binary ~ Budget + Genre + Gender + Awards + Imdb_Rating + Imdb_Votes,data = train_data, family ="binomial")summary(bechdel_model)
# Predictions and evaluationlog_predictions <-predict(bechdel_model, test_data, type ="response")log_predicted_classes <-as.factor(ifelse(log_predictions >0.5, 1, 0))# Ensure predicted classes have the same levels as the actual datalevels(log_predicted_classes) <-levels(test_data$Bechdel_binary)# Confusion matrix for logistic regressionconfusionMatrix(log_predicted_classes, test_data$Bechdel_binary)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 136 85
1 53 65
Accuracy : 0.5929
95% CI : (0.5385, 0.6457)
No Information Rate : 0.5575
P-Value [Acc > NIR] : 0.104023
Kappa : 0.1564
Mcnemar's Test P-Value : 0.008318
Sensitivity : 0.7196
Specificity : 0.4333
Pos Pred Value : 0.6154
Neg Pred Value : 0.5508
Prevalence : 0.5575
Detection Rate : 0.4012
Detection Prevalence : 0.6519
Balanced Accuracy : 0.5765
'Positive' Class : 0
Show the code
# Random Forest # Train random forest modelrf_model <-randomForest( Bechdel_binary ~ Budget + Genre + Gender + Awards + Imdb_Rating + Imdb_Votes,data = train_data, ntree =100, importance =TRUE)# Predictions and evaluationrf_predictions <-predict(rf_model, test_data)# Ensure random forest predictions are factorsrf_predictions <-as.factor(rf_predictions)levels(rf_predictions) <-levels(test_data$Bechdel_binary)# Confusion matrix for random forestconfusionMatrix(rf_predictions, test_data$Bechdel_binary)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 138 78
1 51 72
Accuracy : 0.6195
95% CI : (0.5654, 0.6714)
No Information Rate : 0.5575
P-Value [Acc > NIR] : 0.01214
Kappa : 0.2141
Mcnemar's Test P-Value : 0.02207
Sensitivity : 0.7302
Specificity : 0.4800
Pos Pred Value : 0.6389
Neg Pred Value : 0.5854
Prevalence : 0.5575
Detection Rate : 0.4071
Detection Prevalence : 0.6372
Balanced Accuracy : 0.6051
'Positive' Class : 0
Genre: A consistently strong predictor across both models. Specific genres like Drama, Comedy, and Horror are more likely to pass the Bechdel Test.
Gender: Director gender significantly influences passing the Bechdel Test,when the director is male, the probability of a movie passing the test decrease.
IMDb Rating: Higher IMDb ratings slightly decrease the likelihood of passing the Bechdel Test.
Budget, Awards, and IMDb Votes are less critical in determining whether a film passes the Bechdel Test.
Mean decreased accuracy shows that the most important variable for the accuracy of the model are Genre, gender and IMDb_votes, they are key factor for determining whether a film passes the Bechdel Test.
The accuracy of the models are moderate with random forest slightly outperforming logistic regression (61.95% vs. 59.29% accuracy)
Conclusion
Genre emerges as the most critical predictor across both models, with specific genres like Drama, Comedy, Biography, and Horror significantly associated with passing the test. IMDb Votes also plays a substantial role, indicating that audience engagement correlates with a higher likelihood of passing. Director gender shows moderate importance, with logistic regression suggesting that male-directed films are less likely to pass the test as shown in the EDA part. Awards, however, have minimal predictive power, showing no meaningful relationship with the Bechdel Test outcome.
8. Conclusion
The results of this analysis offer valuable insights into the relationship between the Bechdel Test and various film-related factors, including revenue, awards, and IMDb ratings. Through statistical tests, predictive modeling, and data exploration, several key findings have emerged that address our project goals.
First, our analysis reveals that passing the Bechdel Test does not have a direct, statistically significant effect on a film’s revenue once other factors, such as budget, genre, and IMDb ratings, are controlled for. While initial correlations suggested a weak inverse relationship between passing the test and revenue, these associations were largely confounded by genre and budget. Predictive modeling also confirmed that the Bechdel Test outcome is not independently influenced by revenue but is instead shaped by factors such as genre and director gender, which indirectly affect the test’s results.
Second, films that pass the Bechdel Test do not show a significant advantage in terms of awards won. However, IMDb ratings were moderately higher for films that fail the Bechdel Test, potentially reflecting biases in audience reception or content characteristics. Predictive models developed during the study, such as logistic regression and random forest, highlighted genre and director gender as the strongest predictors of a film passing the Bechdel Test. Specifically, certain genres like Drama, Comedy, Biography, and Horror showed higher likelihoods of passing, while male-directed films were less likely to pass, a trend that may be partially explained by disparities in budgets and creative choices rather than inherent bias.
In terms of predictive modeling, we successfully developed models to estimate a film’s success in terms of revenue and awards, as well as its likelihood of passing the Bechdel Test. The random forest model outperformed logistic regression in predictive power, identifying Genre, IMDb Votes, and Budget as the most influential factors in determining success and test outcomes. These models provided moderate accuracy, highlighting areas where additional variables, such as runtime or content diversity, could enhance predictive capabilities.
Finally, the trend visualization confirmed that genre, budget, and director gender are strongly associated with both Bechdel Test outcomes and economic success metrics. While films that pass the Bechdel Test often align with genres featuring diverse character interactions, their financial and critical success appears more strongly tied to production scale and audience engagement. These findings underline the importance of carefully considering contextual factors when evaluating the economic impact of female representation in film.
8.1 Final Remarks
While the Bechdel Test provides a useful lens for analyzing female representation, its influence on a film’s economic success is intertwined with broader industry dynamics. This study highlights that gender inclusivity in films is often shaped by structural factors, such as budget allocation and genre conventions, rather than directly impacting financial performance. Future research could explore additional predictors, investigate causal pathways, and refine models to better understand the evolving relationship between gender representation and film success.
